Python ValueError:找到样本数不一致的输入变量：[126004200]
在这段代码中，我正在进行时间序列分割，然后使用scikit learn创建用于预测的SVR模型。我的代码是：Python ValueError:找到样本数不一致的输入变量：[126004200],python,scikit-learn,svm,Python,Scikit Learn,Svm,在这段代码中，我正在进行时间序列分割，然后使用scikit learn创建用于预测的SVR模型。我的代码是： from sklearn import preprocessing as pre from sklearn.preprocessing import StandardScaler from sklearn.model_selection import TimeSeriesSplit from sklearn import svm from sklearn.preprocessing i
from sklearn import preprocessing as pre
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import TimeSeriesSplit
from sklearn import svm
from sklearn.preprocessing import MinMaxScaler
X_feature = wind_speed
X_feature = X_feature.reshape(-1, 1)## Reshaping array to be 1D from 2D
y_label = Power
y_label = y_label.reshape(-1,1)
timeseries_split = TimeSeriesSplit(n_splits=3)
for train1, test1 in timeseries_split.split(X_feature):
print("Training data:",train1, "Testing data test:", test1)
train1 = train1.reshape(-1,1)## Reshaping array to be 1D fron 2D
test1 = test1.reshape(-1,1)
timeseries_split = TimeSeriesSplit(n_splits=3)
for train, test in timeseries_split.split(y_label):
print("Training data_1:",train, "Testing data test_1:", test)
scaler =pre.MinMaxScaler(feature_range=(0,1)).fit(train1)
scaled_wind_speed_train = scaler.transform(train1)
print("scaler", scaled_wind_speed_train)
scaled_wind_speed_test = scaler.transform(test1)
SVR_model = svm.SVR(kernel='rbf',C=100,gamma=.001).fit(scaled_wind_speed_train,train)
y_prediction = SVR_model.predict(y_label)
print (y_prediction)
SVR_model.score(scaled_wind_speed_test,train)
Training data: [ 0 1 2 ... 4197 4198 4199] Testing data test: [4200 4201 4202 ... 8397 8398 8399]
Training data: [ 0 1 2 ... 8397 8398 8399] Testing data test: [ 8400 8401 8402 ... 12597 12598 12599]
Training data: [ 0 1 2 ... 12597 12598 12599] Testing data test: [12600 12601 12602 ... 16797 16798 16799]
Training data_1: [ 0 1 2 ... 4197 4198 4199] Testing data test_1: [4200 4201 4202 ... 8397 8398 8399]
Training data_1: [ 0 1 2 ... 8397 8398 8399] Testing data test_1: [ 8400 8401 8402 ... 12597 12598 12599]
Training data_1: [ 0 1 2 ... 12597 12598 12599] Testing data test_1: [12600 12601 12602 ... 16797 16798 16799]
scaler [[0.00000000e+00]
[7.93713787e-05]
[1.58742757e-04]
...
[9.99841257e-01]
[9.99920629e-01]
[1.00000000e+00]]
/home/nbuser/anaconda3_501/lib/python3.6/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.
warnings.warn(msg, DataConversionWarning)
[6153.41834275 6006.33852041 5997.57462806 ... 6569.44075144 6393.55696288
6112.57831243]
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-53-925646f8c16a> in <module>()
43
44 print (y_prediction)
---> 45 SVR_model.score(scaled_wind_speed_test,train)
46
47
~/anaconda3_501/lib/python3.6/site-packages/sklearn/base.py in score(self, X, y, sample_weight)
385 from .metrics import r2_score
386 return r2_score(y, self.predict(X), sample_weight=sample_weight,
--> 387 multioutput='variance_weighted')
388
389
~/anaconda3_501/lib/python3.6/site-packages/sklearn/metrics/regression.py in r2_score(y_true, y_pred, sample_weight, multioutput)
528 """
529 y_type, y_true, y_pred, multioutput = _check_reg_targets(
--> 530 y_true, y_pred, multioutput)
531
532 if sample_weight is not None:
~/anaconda3_501/lib/python3.6/site-packages/sklearn/metrics/regression.py in _check_reg_targets(y_true, y_pred, multioutput)
73
74 """
---> 75 check_consistent_length(y_true, y_pred)
76 y_true = check_array(y_true, ensure_2d=False)
77 y_pred = check_array(y_pred, ensure_2d=False)
~/anaconda3_501/lib/python3.6/site-packages/sklearn/utils/validation.py in check_consistent_length(*arrays)
202 if len(uniques) > 1:
203 raise ValueError("Found input variables with inconsistent numbers of"
--> 204 " samples: %r" % [int(l) for l in lengths])
205
206
ValueError: Found input variables with inconsistent numbers of samples: [12600, 4200]
培训数据：[0 1 2…4197 4198 4199]测试数据测试：[4200 4201 4202…8397 8398 8399]
培训数据：[0 1 2…8397 8398 8399]测试数据测试：[8400 8401 8402…12597 12598 12599]
培训数据：[0 1 2…12597 12598 12599]测试数据测试：[12600 12601 12602…16797 16798 16799]
培训数据_1:[0 1 2…4197 4198 4199]测试数据测试_1:[4200 4201 4202…8397 8398 8399]
培训数据_1:[0 1 2…8397 8398 8399]测试数据测试_1:[8400 8401 8402…12597 12598 12599]
培训数据_1:[0 1 2…12597 12598 12599]测试数据测试_1:[12600 12601 12602…16797 16798 16799]
缩放器[[0.00000000 E+00]
[7.93713787e-05]
[1.58742757e-04]
...
[9.99841257e-01]
[9.99920629e-01]
[1.00000000 E+00]]
/home/nbuser/anaconda3_501/lib/python3.6/site packages/sklearn/utils/validation.py:475:DataConversionWarning:MinMaxScaler将输入数据类型为int64的数据转换为float64。
警告。警告（消息，数据转换警告）
[6153.41834275 6006.33852041 5997.57462806 ... 6569.44075144 6393.55696288
6112.57831243]
---------------------------------------------------------------------------
ValueError回溯（最近一次调用上次）
在（）
43
44打印（y_预测）
--->45 SVR\U模型评分（按比例的风速测试，列车）
46
47
得分中的~/anaconda3_501/lib/python3.6/site-packages/sklearn/base.py（自我、X、y、样本重量）
385来自。指标导入r2_分数
386返回r2评分（y，自我预测（X），样本权重=样本权重，
-->387多输出（方差加权）
388
389
r2_分数中的~/anaconda3_501/lib/python3.6/site-packages/sklearn/metrics/regression.py（y_真、y_pred、样本重量、多重输出）
528 """
529 y_type，y_true，y_pred，多输出=\u check\u reg\u targets(
-->530 y_true，y_pred，多输出）
531
532如果样品重量不是无：
~/anaconda3_501/lib/python3.6/site-packages/sklearn/metrics/regression.py in_check_reg_目标（y_真、y_pred、多重输出）
73
74 """
--->75检查长度是否一致（y_true，y_pred）
76 y_真=检查数组（y_真，确保2d=假）
77 y_pred=检查_数组（y_pred，确保_2d=False）
检查中的~/anaconda3\u 501/lib/python3.6/site-packages/sklearn/utils/validation.py一致长度（*数组）
202如果len（uniques）>1：
203 raise VALUETERROR（“找到的输入变量的数量不一致”）
-->204“样本：%r”%[int（l）表示长度为l的样本]）
205
206
ValueError:找到样本数不一致的输入变量：[126004200]
我相信我的错误在：
SVR\u model.score（按比例的风速测试，火车）SVR_model.score(scaled_wind_speed_test,test)
假设我正确理解了您的代码，下面这行应该可以修复错误
SVR_model.score(scaled_wind_speed_test,test)
你好你能缩进问题中的代码使它更可读吗？此外，显然还有一些新词缺失，添加它们将有助于人们理解和回答。此外，是否存在问题线路
SVR\u模型得分（按比例的风速测试，列车）np.shape（按比例的风速测试）列车scorescoreSVR\u模型得分（按比例的风速测试，列车）np.shape（按比例的风速测试）列车scorescore